Data difference guided image capturing

ABSTRACT

Methods and apparatuses are disclosed. Previously stored images of one or more geographic areas may be viewed by online users. A new low-resolution image may be acquired and aspects of the new low-resolution image may be compared with a corresponding one of the previously stored images to determine an amount of change. A determination may be made regarding whether to acquire a new high-resolution image based on the determined amount of change and a freshness score associated with the one of the previously stored images. In another embodiment, a new image may be captured and corresponding location data may be obtained. A corresponding previously stored image may be obtained and compared with the new image to determine an amount of change. The new image may be uploaded to a remote computing device based on the determined amount of change and a freshness score of the previously stored image.

BACKGROUND

Online mapping services, such as, for example, Bing® Maps (Bing is aregistered trademark of Microsoft Corporation of Redmond, Wash.) orGoogle® Maps (Google is a registered trademark of Google Inc. ofMountain View, Calif.), strive to have an extensive representation ofEarth having maximum coverage, freshness and depth. However, maintainingmaximum coverage is expensive. For example, acquisition of new highquality image data for a geographic image database is usually quitecostly. For example, when acquiring high-resolution aerial images, onemust incur a cost for a plane to travel over a geographic area tocapture high-resolution image data. Typically, the cost for acquiringthe high-resolution aerial image data is quite expensive. As a result,high-resolution aerial image data may not be acquired frequently andonline high quality image data may be several years old.

When acquiring street-level images, vehicles equipped with imagecapturing devices, such as, for example, cameras or other devices, aswell as location sensors, may travel along streets to scan and collectthe street-level images and corresponding location data. Each of thevehicles may have a storage device for storing the captured street-levelimages and the corresponding location data and may periodically uploadall of the captured street-level image data to one or more remotecomputing devices for processing. In some cases, the capturedstreet-level image data may be uploaded only at an end of each day,thereby requiring each of the vehicles to have a storage device capableof storing up to one day's amount of collected street-level images andlocation data. When a storage device in a vehicle has reached itscapacity, no additional street-level images and location data may becaptured. The one or more computing devices may process all of theuploaded captured street-level image data, from each of the vehicles, inorder to update coverage of one or more geographic area.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In an embodiment consistent with the subject matter of this disclosure,low-resolution aerial images of one or more geographic areas may beacquired. A computing device may compare each of the low-resolutionaerial images with a respective corresponding previously storedhigh-resolution aerial image to determine an amount of change. Thecomputing device may determine whether a new high-resolution aerialimage is to be obtained to replace the respective correspondingpreviously stored high-resolution aerial image based on the determinedamount of change and a freshness score of the respective correspondingpreviously stored high-resolution aerial image. In one implementation,when the computing device determines that a new high-resolution aerialimage is to be obtained, the computing device may hallucinate asynthetic high-resolution aerial image, based on a respective recentlow-resolution aerial image, to replace the respective correspondingpreviously stored high-resolution aerial image until the newhigh-resolution aerial image is provided.

In another embodiment and image capturing device, location sensors, anda computing device may be placed in a vehicle. The vehicle may travelwithin one or more geographic areas while street-level images andcorresponding location data are captured. The computing device mayreceive the captured street-level images and the corresponding locationdata. For each of the captured street-level images, the computing devicemay request and receive a corresponding previously stored image (orinformation that may be more compressed, but enables detection ofchanges) and an associated freshness score from a remote computingdevice. The computing device may compare aspects of the respectivecaptured street-level image with the corresponding previously storedimage to determine an amount of change. Based on the determined amountof change and the freshness score, the computing device may make adetermination either to upload the respective captured street-levelimage to the remote computing device, or to discard the respectivecaptured street-level image. In variations of this embodiment, differenttechniques may be employed to limit effects of certain objects that mayappear in the respective captured street-level image from affecting thedetermined amount of change.

DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionis discussed below and will be rendered by reference to specificembodiments thereof which are illustrated in the appended drawings.Understanding that these drawings depict only typical embodiments andare not therefore to be considered to be limiting of its scope,implementations will be described and explained with additionalspecificity and detail through the use of the accompanying drawings.

FIG. 1 illustrates an exemplary operating environment for variousembodiments consistent with the subject matter of disclosure.

FIG. 2 is a functional block diagram of an exemplary computing device,which may be used to implement embodiments consistent with the subjectmatter of this disclosure.

FIG. 3 illustrates a flowchart of an exemplary process in oneembodiment.

FIG. 4 illustrates a flowchart of an exemplary process in a variation ofthe embodiment of FIG. 3.

FIG. 5 is a flowchart of an exemplary process which may be implementedin a second embodiment.

DETAILED DESCRIPTION

Embodiments are discussed in detail below. While specificimplementations are discussed, it should be understood that this is donefor illustration purposes only. A person skilled in the relevant artwill recognize that other components and configurations may be usedwithout parting from the spirit and scope of the subject matter of thisdisclosure.

Overview

In various embodiments, methods and apparatuses for guided datacapturing are disclosed. In one embodiment, low-resolution aerial imagedata, including but not limited to low-resolution satellite image data,may be acquired. A cost for acquiring low-resolution aerial image data,such as low-resolution satellite image data or other low-resolutionaerial image data, is significantly less expensive than a cost foracquiring high-resolution aerial image data. For example, road vectordata, as published by suppliers such as, for example, NAVTECH® (NAVTECHis a registered trademark of Navigation Technologies Corporation ofRosemont, Ill.) or Teleatlas, may be updated monthly. Satellite imagery,such as imagery provided by Landsat or Spot Image are commerciallyavailable at low cost and are frequently updated. For example, everypoint on Earth is updated more than once each month.

Previously-obtained high-resolution aerial image data of a number ofgeographic areas may be stored for online use. Each item of thepreviously-obtained high-resolution aerial image data may have arespective freshness score, which may be a function of time. Thefreshness score may be indicative of a probability that a newhigh-resolution image, or image data, is to be acquired in order toupdate coverage of a corresponding geographic area. For example, at amaximum age, Kmax, the freshness score of items of thepreviously-obtained high-resolution aerial image data may have a value,such as, for example, 1, indicating a 100% probability that a plane willbe sent to acquire new high-resolution aerial image data to update thecoverage of a corresponding geographic area. In one embodiment, Kmax maybe five years. However, for various geographical areas, Kmax may haveother suitable values, such as, for example, 30 months, 30 days, oranother value.

One or more computing devices may compare aspects of a recently acquiredlow-resolution aerial image with aspects of a correspondingpreviously-obtained high-resolution aerial image. The one or morecomputing devices may determine whether to order a new high-resolutionaerial image to replace the corresponding previously-obtainedhigh-resolution aerial image based on a determined amount of change anda freshness score associated with the corresponding previously-obtainedhigh-resolution aerial images.

Many vehicles currently drive through streets of a geographic area on adaily basis. The vehicles may include, but are not be limited to,vehicles for transportation companies, mail delivery vehicles, taxis,waste management vehicles, as well as other vehicles. Each of thevehicles may be equipped with one or more image capturing devices, oneor more location sensors, and a computing device with a storage devicefor storing captured image data. The one or more image capturing devicesand the one or more location sensors may provide recently capturedstreet-level image data and corresponding location data to the computingdevice. The computing device may request one or more remote computingdevices to provide previously acquired and stored image datacorresponding to the location data provided by the one or more locationsensors. The computing device may receive, from the one or more remotecomputing devices, the requested previously acquired and stored imagedata and a corresponding freshness score, which the computing device maydisplay.

The computing device may then compare various aspects of the recentlycaptured street-level image data with the requested previously acquiredand stored image data to determine an amount of change. The amount ofchange may be focused on man-made changes including, but not limited to,a change in appearance of roads and buildings, or agricultural changeswithin a geographic area, as well as other types of changes. Forexample, aspects of the requested previously acquired and stored imagedata and the recently captured street-level image data, such as, forexample, spectral reflectance, textures, color distribution as well asother aspects, may be compared.

An amount of change of the various aspects and the freshness score ofthe previously acquired and stored image data may be used to determinewhether the recently captured street-level image data may be uploaded tothe one or more remote computing devices in order to update coverage ofthe geographic area, or whether the recently captured street-level imagedata may be discarded. In some embodiments, when the previously acquiredand stored image data has a high freshness score, indicating that thepreviously acquired and stored image data is relatively old, a firstminimum amount of change may result in a determination to upload therecently captured street-level image data to the one or more remotecomputing devices for processing. When the previously acquired andstored image data has a low freshness score, indicating that thepreviously acquired and stored image data is relatively young, thedetermination to upload the recently captured street-level image data tothe one or more remote computing devices for processing may have aminimum amount of change for the determination, which is larger than theminimum amount of change with respect to the relatively old previouslyacquired and stored image data. That is, when the previously acquiredand stored image data is relatively young, a first minimum amount ofchange for a determination to be made to upload the recently capturedstreet-level image data to the one or more remote computing devices islarger than a second minimum amount of change, with respect to therelatively old previously acquired and stored image data.

Exemplary Operating Environment

FIG. 1 is a functional block diagram of an exemplary operatingenvironment 100 for various embodiments. Exemplary operating environment100 may include a first computing device 102, one or more secondcomputing devices 104, an image capturing device 106, a third computingdevice 108, a satellite 110, a plane 112, and a network 114.

First computing device 102 may be a computing device of a user whowishes to view image data of one or more geographic locations. Firstcomputing device 102 may be a desktop personal computer, a notebookpersonal computer, a personal digital assistant (PDA), or other type ofcomputing device. One or more second computing devices 104 may includeone server or multiple servers connected to one another in a serverfarm, one or more computing devices from a group of computing devicesincluding a desktop personal computer, a notebook personal computer, aPDA, and other computing devices.

Image capturing device 106 may be a digital camera or other type ofdigital image capturing device. Third computing device 108 may includeone or more computing devices selected from a group including notebookpersonal computers, one or more desktop personal computers, and one ormore other types of computing devices. Third computing device 108 mayhave a wired or wireless connection with image capturing device 106. Insome embodiments, image capturing device 106 and one or more thirdcomputing devices 108 may be placed within a vehicle, such as, forexample, a car, a truck, a bus, or other type of vehicle.

Network 114 may include a local area network, a wide area network, apacket switching network, an ATM network, a frame relay network, a fiberoptic network, a public switched telephone network, a wireless network,a wired network, another type of network, or any combination thereof.First computing device 102 and second computing device 104 may beconnected to network 114. Third computing device 108 may be connected tosecond computing device 104 either through a wireless connection tonetwork 114 or through a connection via another network or group ofnetworks.

Satellite 110 may include one or more image capturing devices foracquiring low-resolution aerial images, or image data, with respect to anumber of geographic areas. Plane 112 or satellite 110 may include oneor more image capturing devices for acquiring high-resolution aerialimages, or image data with respect to the number of geographic areas.Acquired low resolution aerial images, or image data, captured bysatellite 110 may be provided to second computing device 104. Similarly,one or more high-resolution aerial images, or image data, from plane 112or satellite 110 may be provided to second computing device 104.

Exemplary Computing Device

FIG. 2 is a block diagram of an exemplary computing device 200, whichmay be employed to implement one or more embodiments consistent with thesubject matter of this disclosure. Exemplary computing device 200 mayinclude a bus 210, a processor 220, a memory 230, an output device 240,a storage device 250, an input device 260, and a communication interface270. Bus 210 may connect processor 220, memory 230, output device 240,storage device 250, input device 260 and communication interface 270.

Processor 220 may include one or more conventional processors thatinterpret and execute instructions. Memory 230 may include a RandomAccess Memory (RAM), a Read Only Memory (ROM), and/or other type ofdynamic or static storage device that stores information andinstructions for execution by processor 120. The RAM, or the other typeof dynamic storage device, may store instructions as well as temporaryvariables or other intermediate information used during execution ofinstructions by processor 120. The ROM, or the other type of staticstorage device, may store static information and instructions forprocessor 120.

Output device 240 may include a display device such as, for example, aplasma display, a liquid crystal display, a cathode ray tube (CRT),other type of display device, or other type of output device.

Storage device 250 may include a storage medium including, but notlimited to, optical disc, flash RAM, magnetic tape, magnetic disk, orother type of storage device. Storage device 250 may have stored thereonan operating system, hardware configuration information, and/or one ormore executable applications.

Input device 260 may include a keyboard, a pointing device, or othertype of input device. The pointing device may be, for example, acomputer mouse, a trackball, a user's finger or other type of pointingdevice.

Communication interface 170 may include one or more transceivers fortransmitting and receiving data via a wireless or a wired connection.

A number of exemplary computing devices 200 may be used to implementfirst computing device 102, second computing device 104, and/or thirdcomputing device 108. When exemplary computing device 200 implementsfirst computing device 102, communication interface 270 may have a wiredor wireless connection to network 114. When exemplary computing device200 implements second computing device 104, communication interface 270may have a wired or wireless connection to network 114, and when thirdcomputing device 108 is not connected to second computing device 104 vianetwork 114, second computing device 104 may further include a secondcommunication interface to a second network connected to third computingdevice 108. When exemplary computing device 200 implements thirdcomputing device 108, communication interface 270 may include a firstcommunication interface to interface with image capturing device 106 anda second communication interface to interface with the second computingdevice 104 via either network 114 or another network.

Exemplary Processing

FIG. 3 is a flowchart illustrating exemplary processing in an embodimentconsistent with the subject matter of this disclosure. The process maybegin with second computing device 104 obtaining a recently capturedlow-resolution aerial image, or image data, from satellite 110 andcorresponding location information with respect to a geographic area(act 302). Second computing device 104 may then obtain a correspondingpreviously acquired and stored high-resolution aerial image, or imagedata, along with an associated freshness score (act 304).

Second computing device may then compare various aspects of the recentlycaptured low-resolution aerial image, or image data, with the previouslyacquired and stored high-resolution aerial image to determine an amountof change (act 306). In order to reduce differences caused by seasonalchanges, in one implementation, the previously acquired and storedhigh-resolution aerial image and the recently acquired low-resolutionaerial image both may have been acquired during a same season ofdifferent years or during a same month of different years. In someimplementations, the previously acquired and stored high-resolutionaerial image and the recently acquired low-resolution aerial image bothmay have been acquired by a same satellite. The amount of change may befocused on agricultural changes by determining an amount of change withrespect to color distribution. Further, the amount of change may befocused on man-made changes including, but not limited to, changes inappearance of roads and buildings, which may be determined based onimage appearance, spectral reflectance, textures, as well as otheraspects of the images.

Next, second computing device 104 may determine whether to order ahigh-resolution aerial image based on the determined amount of changeand the freshness score of the previously acquired and stored image, orimage data (act 308). As previously mentioned, with respect to an olderpreviously acquired and stored image, or image data, a minimum amount ofchange for a determination to order high-resolution aerial images issmaller than a minimum amount of change, with respect to a newerpreviously acquired and stored image, or image data, for thedetermination to order high-resolution aerial images.

If, during act 308, second computing device 104 determines that ahigh-resolution aerial image is to be ordered, then second computingdevice 104 may provide an indication that the high-resolution aerialimage is to be ordered, or second computing device 104 may automaticallyplace an order for the high-resolution aerial image (act 310). Secondcomputing device 104 may then hallucinate a new synthetic aerial imagebased on the recently acquired low-resolution aerial image (act 312). Insome implementations, the synthetic aerial image, when rendered, mayhave a cartoon-like appearance. The second computing device 104 may thentemporarily replace the previously acquired and stored aerial image withthe new synthetic aerial image until a new high-resolution aerial imageis provided (act 314). Thus, if a user makes a request to view an aerialimage with respect to a geographic area of a corresponding previouslyacquired and stored aerial image, or image data, that has been replacedby a hallucinated synthetic aerial image, the user may be presented withthe synthetic aerial image. The user may also be presented with acorresponding freshness score of the replaced previously acquired andstored aerial image, or image data.

If during act 308, second computing device 104 determines thathigh-resolution aerial images are not to be ordered, or after secondcomputing device 104 performs act 314, second computing device 104 maydetermine whether any additional recently captured low-resolution aerialimages exist (act 316). If no additional recently capturedlow-resolution aerial images exist, then the process may be completed.Otherwise, second computing device 104 may obtain a next recentlycaptured low-resolution aerial image (act 318) and may again performacts 304-318.

In some embodiments, a determination to order high-resolution aerialimages may be based on non-image data including, but not limited to,data from maps, data indicating a significant increase or decrease incell phone usage in at least a portion of a geographic area, articlesconcerning new construction, demolition, or land use changes, as well asother non-image data.

FIG. 4 is a flowchart illustrating exemplary processing in animplementation in which a determination to order high-resolution aerialimages is further based on non-image data. The process may begin withsecond processing device 104 obtaining, or being provided with,non-image data related to previously acquired and stored aerial images(act 402). Second computing device 104 may then obtain a freshness scoreof a corresponding previously acquired and stored aerial image (act404).

Second computing device 104 may then make a determination whether toorder high-resolution aerial images based on the freshness score and thenon-image data (act 406). As an example, non-image data may have anassociated value. A change with respect to a major road may have ahigher value than a change with respect to a side street. A change withrespect to a group of buildings on a same street may have a higher valuethan a change with respect to a single building on a same street, and soon. Thus, the associated value of non-image data may be a sum of valuesassociated with various types of changes. A minimum value with respectto making a determination to order an aerial image may be lower when afreshness score for a corresponding previously acquired and storedaerial image indicates an older image age as compared to a minimum valuewith respect to making the determination to order aerial images when thefreshness score for the corresponding previously acquired and storedaerial image indicates a younger image age.

If, during act 406, second computing device determines that thehigh-resolution aerial image is to be ordered, then second computingdevice 104 may indicate that the high-resolution aerial image is to beordered, or may place an order for the high-resolution aerial image ofthe geographic area (act 408).

After performing act 408, or after performing act 406 and determiningthat a new hi-resolution aerial image is not to be ordered, secondcomputing device 104 may determine whether additional non-image dataexists to be processed (act 410). If additional non-image data does notexist, then the process is completed. Otherwise, second computing device104 may obtain a next item of non-image data related to a previouslystored aerial image, or image data (act 412). Second computing device104 again may perform acts 404-412 until no additional non-image dataexists to be processed.

FIG. 5 is a flowchart illustrating exemplary processing in an embodimentin which street-level images may be acquired. The process may begin withthird computing device 108 obtaining a recently captured street-levelimage, or image data, from image capturing device 106, and location datafrom one or more location sensors (act 502). Third computing device 108,image capturing device 106 and the one or more location sensors may belocated within a moving vehicle.

Third computing device 108 may request and receive a correspondingonline image, or image data, and an associated freshness score fromsecond processing device 106 (act 504). Third computing device 108 maypresent, or display, the associated freshness score (act 506). Thirdcomputing device 108 may then compare aspects of the correspondingonline image, or image data, and the recently captured street-levelimage, or image data to determine an amount of change between thecorresponding online image, or image data, and the recently capturedstreet-level image, or image data (act 508). Various modalities may beemployed by third computing device 108 to better sense a structure and areflectance of the recently captured street-level image, or image data.The modalities may include, but not be limited to: color video; stereovideo; Light Detection And Ranging (LIDAR), which is a measuring systemthat uses light from a laser to detect and locate objects based on asame principle used by radar; and near infrared (IR) input. Techniquesincluding, but not limited to, face detection, pedestrian detection,vehicle detection, motion detection and detection of foreground objectsmay be employed to limit change detection to static buildings appearingin a street-level image rather than to cars and/or people. In someimplementations, techniques may be employed including, but not limitedto, text recovery from natural images on street signs and/or businesssigns for recovering text and comparing the recovered text to known datato detect changes.

Third computing device 108 may then determine whether to upload therecently captured street-level image, or image data, to second computingdevice 104 based on the determined amount of change and the freshnessscore (act 510). With respect to an older previously acquired and storedimage, or image data, a minimum amount of change for a determination toupload the recently captured street-level image, or image data issmaller than a minimum amount of change, with respect to a youngerpreviously acquired and stored street-level image, or image data, forthe determination to upload the recently captured street-level image.

If, during act 510, third computing device 108 determines that therecently captured street-level image, or image data, is to be uploadedto second computing device 104, then third computing device 108 mayupload the recently captured street-level image, or image data, tosecond computing device 104. Otherwise, third computing device 108 maydiscard the recently captured street-level image, or image data, and maydetermine whether there are any additional recently capturedstreet-level images, or image data, to process (act 514). If noadditional recently captured street-level images, or image data exists,then the process may be completed. Otherwise, third computing device 108may receive, or obtain, a next recently captured image, or image data(act 516), and may repeat acts 504-516 until no additional recentlycaptured street-level image, or image data exists.

In other embodiments, a multitude of online images, or image data,captured by users may be employed in a same manner as the recentlycaptured images, or image data, in the above described embodiments inorder to determine whether to update previously acquired and storedimages, or image data. For example, approximately two million imagesfrom Flicker, an online photo sharing service, have been matched to aworld model. Change detection may be used to guide processing of theapproximately two million images, thereby focusing effort on only onesof the approximately 2 million images that include new information.

Conclusion

Various embodiments were described in which a recently capturedlow-resolution aerial image, or a street-level image, may be comparedwith a corresponding previously acquired and stored high-resolutionimage to determine an amount of change. The determined amount of changeand a freshness score associated with the previously acquired and storedhigh-resolution image may be used to make a determination whether toorder a high-resolution aerial image. Using change detection, onlyrecently captured images, which include new information, may be used byone or more remote computing devices to update corresponding previouslyacquired and stored images.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms for implementing the claims.

Other configurations of the described embodiments are part of the scopeof this disclosure. For example, in other embodiments, an order of actsperformed by a process may be different and/or may include additional orother acts.

Accordingly, the appended claims and their legal equivalents defineembodiments, rather than any specific examples given.

What is claimed is:
 1. A method, comprising: resolving a freshness scorefor an acquired high-resolution aerial image of a geographic area, thescore indicative of a probability that a new high-resolution image ofthe geographic area is needed so as to update coverage of the geographicarea; identifying a recently acquired low-resolution aerial image of thegeographic area; determining an amount of change in the geographic areasince the high-resolution image was acquired by comparing, by acomputing device, one or more aspects of the identified recentlyacquired low-resolution aerial image and of the stored high-resolutionaerial image; and deciding to update the coverage of the geographic areaby ordering a new high-resolution aerial image, based on the freshnessscore and the determined amount of change.
 2. The method of claim 1,further comprising resolving a synthetic high-resolution image from theobtained recent low-resolution aerial image, when a result of thedeciding is affirmative.
 3. The method of claim 1, wherein thedetermining an amount of change further comprises removing person-sizedobjects from the one or more aspects that are compared.
 4. The method ofclaim 1, wherein the one or more aspects reflect an amount of change tostatic buildings.
 5. The method of claim 1, wherein the determining anamount of change further comprises: recovering text from the capturedimage data; and comparing the recovered text from the captured imagedata with known data.
 6. A method, comprising: resolving a freshnessscore for an acquired high-resolution image of a location, the scoreindicative of a probability that a new high-resolution image of thelocation is needed so as to update coverage of the location; identifyinga recently acquired low-resolution image of the location; determining anamount of change in the location since the high-resolution image wasacquired by comparing, by a computing device, at least one aspect of theidentified recently acquired low-resolution image and at least oneaspect of the stored high-resolution image; and deciding to update thecoverage of the location by ordering a new high-resolution image, basedon the freshness score and the determined amount of change.
 7. Themethod of claim 6, wherein at least one of the recently acquiredlow-resolution image and the acquired high-resolution image is an aerialimage.
 8. The method of claim 6, wherein at least one of the recentlyacquired low-resolution image and the acquired high-resolution image isa street level image.
 9. The method of claim 6, wherein at least one ofthe recently acquired low-resolution image and the acquiredhigh-resolution image is an online image.
 10. The method of claim 6,wherein the one or more aspects is a color distribution at the location,a spectral reflectance at the location, or textures at the location. 11.The method of claim 6, wherein the one or more aspects reflects man-madechanges at the location.
 12. The method of claim 11, wherein the one ormore aspects includes a change in an appearance of a road at thelocation, a change in appearance of buildings at the location, oragricultural changes at the location.
 13. The method of claim 6, whereinthe determining comprises analyzing non-image data.
 14. The method ofclaim 13, wherein the non-image data includes data from articlesconcerning new construction at the location, articles concerningdemolition at the location, or articles concerning changes in land useat the location.
 15. The method of claim 6, wherein the determining anamount of change includes analyzing non-image data, the non-image datahaving an associated summation value that is a sum of respective valuesassociated with members of a set of various types of changes at thelocation.
 16. A computing device comprising: at least one processor; abus; and a memory connected to the at least one processor via the bus;the memory including instructions for the at least one processor toperform a method comprising: resolving a freshness score for an acquiredhigh-resolution aerial image of a geographic area, the score indicativeof a probability that a new high-resolution image of the geographic areais needed so as to update coverage of the geographic area; identifying arecently acquired low-resolution aerial image of the geographic area;determining an amount of change in the geographic area since thehigh-resolution image was acquired by comparing, by a computing device,one or more aspects of the identified recently acquired low-resolutionaerial image and of the stored high-resolution aerial image; anddeciding to update the coverage of the geographic area by ordering a newhigh-resolution aerial image, based on the freshness score and thedetermined amount of change.
 17. A computing device comprising: at leastone processor; a bus; and a memory connected to the at least oneprocessor via the bus, the memory including instructions for the atleast one processor to perform a method comprising: resolving afreshness score for an acquired high-resolution image of a location, thescore indicative of a probability that a new high-resolution image ofthe location is needed so as to update coverage of the location;identifying a recently acquired low-resolution image of the location;determining an amount of change in the location since thehigh-resolution image was acquired by comparing, by a computing device,at least one aspect of the identified recently acquired low-resolutionimage and at least one aspect of the stored high-resolution image; anddeciding to update the coverage of the location by ordering a newhigh-resolution image, based on the freshness score and the determinedamount of change.